This dataset presents a comprehensive overview of rental property listings across multiple cities in the United Arab Emirates, including Abu Dhabi, Dubai, Sharjah, Ajman, Ras Al Khaimah, Umm Al Quwain, and Al Ain. Compiled from bayut.com, it is a valuable resource for Data Analysts, Data Scientists, and Researchers looking to explore real estate trends, rental pricing patterns, or urban development studies in the UAE.
Each entry in the dataset represents a rental property listing with details about the property's features, rental terms, and location specifics. This primary and unique dataset is designed for analysis and can be used to generate insights into the rental market dynamics of the UAE.
This dataset is open for public use and is particularly suited for:
Analyzing trends in the rental market. Studying the geographical distribution of rental properties. Comparing rental prices across different cities and property types. Developing machine learning models to predict rental prices or classify property types.
import pandas as pd
# Load your dataset
df = pd.read_csv('dubai_properties.csv')
df.columns
df
| Address | Rent | Beds | Baths | Type | Area_in_sqft | Rent_per_sqft | Rent_category | Frequency | Furnishing | Purpose | Posted_date | Age_of_listing_in_days | Location | City | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | The Gate Tower 2, The Gate Tower, Shams Gate D... | 124000 | 3 | 4 | Apartment | 1785 | 69.467787 | Medium | Yearly | Unfurnished | For Rent | 2024-03-07 | 45 | Al Reem Island | Abu Dhabi | 24.493598 | 54.407841 |
| 1 | Water's Edge, Yas Island, Abu Dhabi | 140000 | 3 | 4 | Apartment | 1422 | 98.452883 | Medium | Yearly | Unfurnished | For Rent | 2024-03-08 | 44 | Yas Island | Abu Dhabi | 24.494022 | 54.607372 |
| 2 | Al Raha Lofts, Al Raha Beach, Abu Dhabi | 99000 | 2 | 3 | Apartment | 1314 | 75.342466 | Medium | Yearly | Furnished | For Rent | 2024-03-21 | 31 | Al Raha Beach | Abu Dhabi | 24.485931 | 54.600939 |
| 3 | Marina Heights, Marina Square, Al Reem Island,... | 220000 | 3 | 4 | Penthouse | 3843 | 57.246942 | High | Yearly | Unfurnished | For Rent | 2024-02-24 | 57 | Al Reem Island | Abu Dhabi | 24.493598 | 54.407841 |
| 4 | West Yas, Yas Island, Abu Dhabi | 350000 | 5 | 7 | Villa | 6860 | 51.020408 | High | Yearly | Unfurnished | For Rent | 2024-02-16 | 65 | Yas Island | Abu Dhabi | 24.494022 | 54.607372 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 73737 | Al Huboob 1, Al Salamah, Umm Al Quwain | 14000 | 0 | 1 | Apartment | 419 | 33.412888 | Low | Yearly | Unfurnished | For Rent | 2023-12-14 | 129 | Al Salamah | Umm Al Quwain | 25.493412 | 55.575994 |
| 73738 | Umm Al Quwain Marina, Umm Al Quwain | 14000 | 0 | 1 | Apartment | 500 | 28.000000 | Low | Yearly | Unfurnished | For Rent | 2023-12-14 | 129 | Umm Al Quwain Marina | Umm Al Quwain | 25.527959 | 55.606527 |
| 73739 | King Faisal Street, Umm Al Quwain | 50000 | 3 | 4 | Apartment | 2000 | 25.000000 | Low | Yearly | Unfurnished | For Rent | 2024-01-02 | 110 | King Faisal Street | Umm Al Quwain | NaN | NaN |
| 73740 | Al Maqtaa, Umm Al Quwain | 37000 | 1 | 2 | Apartment | 989 | 37.411527 | Low | Yearly | Unfurnished | For Rent | 2023-10-23 | 181 | Al Maqtaa | Umm Al Quwain | NaN | NaN |
| 73741 | Al Rass, Umm Al Quwain | 11000 | 0 | 1 | Apartment | 300 | 36.666667 | Low | Yearly | Unfurnished | For Rent | 2024-02-12 | 69 | Al Rass | Umm Al Quwain | NaN | NaN |
73742 rows × 17 columns
# Explore the data
print(df.head()) # Display the first few rows
Address Rent Beds Baths \
0 The Gate Tower 2, The Gate Tower, Shams Gate D... 124000 3 4
1 Water's Edge, Yas Island, Abu Dhabi 140000 3 4
2 Al Raha Lofts, Al Raha Beach, Abu Dhabi 99000 2 3
3 Marina Heights, Marina Square, Al Reem Island,... 220000 3 4
4 West Yas, Yas Island, Abu Dhabi 350000 5 7
Type Area_in_sqft Rent_per_sqft Rent_category Frequency \
0 Apartment 1785 69.467787 Medium Yearly
1 Apartment 1422 98.452883 Medium Yearly
2 Apartment 1314 75.342466 Medium Yearly
3 Penthouse 3843 57.246942 High Yearly
4 Villa 6860 51.020408 High Yearly
Furnishing Purpose Posted_date Age_of_listing_in_days Location \
0 Unfurnished For Rent 2024-03-07 45 Al Reem Island
1 Unfurnished For Rent 2024-03-08 44 Yas Island
2 Furnished For Rent 2024-03-21 31 Al Raha Beach
3 Unfurnished For Rent 2024-02-24 57 Al Reem Island
4 Unfurnished For Rent 2024-02-16 65 Yas Island
City Latitude Longitude
0 Abu Dhabi 24.493598 54.407841
1 Abu Dhabi 24.494022 54.607372
2 Abu Dhabi 24.485931 54.600939
3 Abu Dhabi 24.493598 54.407841
4 Abu Dhabi 24.494022 54.607372
print(df.describe()) # Summary statistics
Rent Beds Baths Area_in_sqft Rent_per_sqft \
count 7.374200e+04 73742.000000 73742.000000 73742.000000 73742.000000
mean 1.479250e+05 2.162811 2.650213 2054.053552 88.057754
std 3.069658e+05 1.578155 1.632997 3003.919252 66.534400
min 0.000000e+00 0.000000 1.000000 74.000000 0.000000
25% 5.499900e+04 1.000000 2.000000 850.000000 39.977778
50% 9.800000e+04 2.000000 2.000000 1334.000000 71.428571
75% 1.700000e+05 3.000000 3.000000 2130.000000 118.483412
max 5.500000e+07 12.000000 11.000000 210254.000000 2182.044888
Age_of_listing_in_days Latitude Longitude
count 73742.000000 73023.000000 73023.000000
mean 74.261547 24.918929 55.053133
std 72.346767 0.569356 0.653722
min 11.000000 15.175847 43.351928
25% 30.000000 24.493598 54.607372
50% 52.000000 25.078641 55.238209
75% 95.000000 25.197978 55.367138
max 2276.000000 25.920310 56.361294
print(df.isnull().sum()) # Check for missing values
Address 0 Rent 0 Beds 0 Baths 0 Type 0 Area_in_sqft 0 Rent_per_sqft 0 Rent_category 0 Frequency 0 Furnishing 0 Purpose 0 Posted_date 0 Age_of_listing_in_days 0 Location 0 City 0 Latitude 719 Longitude 719 dtype: int64
The dataset consists of 73,742 rows and 17 columns.
The primary features of interest in this dataset are:
Address: Full address of the property.
Rent: The annual rent price in AED.
Beds: Number of bedrooms in the property.
Baths: Number of bathrooms in the property.
Type: Type of property (e.g., Apartment, Villa, Penthouse).
Area_in_sqft: Total area of the property in square feet.
Rent_per_sqft: Rent price per square foot, calculated as Rent divided by Area_in_sqft.
Rent_category: Categorization of the rent price (Low, Medium, High) based on thresholds.
Frequency: Rental payment frequency, which is consistently 'Yearly'.
Furnishing: Furnishing status of the property (Furnished, Unfurnished).
Purpose: The purpose of the listing, typically 'For Rent'.
Posted_date: The date the property was listed for rent.
Age_of_listing_in_days: The number of days the listing has been active since it was posted.
Location: A more specific location within the city where the property is located.
City: City in which the property is situated.
Latitude, Longitude: Geographic coordinates of the property.
The following features are likely to be helpful in further investigating the features of interest:
City: City in which the property is situated.
Number of Properties: This can provide insights into market trends and availability.
Latitude, Longitude: Geographic coordinates for spatial analysis.
Address: Specific location details.
Furnishing: Furnishing status of the property.
Type: Type of property (e.g., Apartment, Villa, Penthouse).
We will use this visualization to answer the following questions:
What is the number of rental properties in each city?
Where are the rental properties located within the UAE?
Which city has the highest average rent?
How does the type of property affect the rent price?
import plotly.express as px
# What is the number of rental properties in each city?
city_distribution = df['City'].value_counts().reset_index()
city_distribution.columns = ['City', 'Number of Properties']
fig = px.bar(city_distribution, x='City', y='Number of Properties',
title='Rental Property Distribution by City',
labels={'Number of Properties': 'Number of Properties'})
fig.update_layout(xaxis_title='City', yaxis_title='Number of Properties')
fig.show()
#Where are the rental properties located within the UAE?
fig = px.scatter_mapbox(df, lat="Latitude", lon="Longitude", hover_name="Address", hover_data=["Rent", "Beds", "Baths"],
color_discrete_sequence=["fuchsia"], zoom=5, height=600)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(title='Rental Properties Locations in UAE')
fig.show()
import matplotlib.pyplot as plt
import seaborn as sns
# Which city has the highest average rent?
plt.figure(figsize=(10, 8))
sns.scatterplot(x='City', y='Rent', data=df, hue='Furnishing')
plt.title('Rent vs. City by Furnishing')
plt.xlabel('City')
plt.ylabel('Rent')
plt.show()
# How does the type of property affect the rent price?
plt.figure(figsize=(16, 12))
sns.boxplot(x='Type', y='Rent', data=df)
plt.title('Rent Distribution by Type')
plt.xlabel('Type')
plt.ylabel('Rent')
plt.show()
From these results, we can conclude that:
Dubai has the highest number of rental properties and the highest average rent, while Fujairah has the lowest number of rental properties and the lowest average rent.
Within Dubai, the map shows that the highest rent is 1,100,000 AED in Umm Al Quwain Marina, Umm Al Quwain, and the lowest rent is 14,000 AED in Jebel Ali. Therefore, Umm Al Quwain Marina, Umm Al Quwain, appears to be the most profitable area for renters.
It is evident that villas are the most common type of property available for rent.